For MNIST dataset, we artificially imbalance the class “8”, to have only 500 samples. For SVHN we imbalance the same class as well. We decide to imbalance class 8 to maintain consistency with [1]}. The ratio of imbalance is set to 1:10 i.e for every 10 images in each class, there was only 1 image in class 8. We apply this imbalance for the train set as well as the test set. In total, we sample 5000 training and 1000 test samples for each class except class 8. Class 8 gets 500 training and 100 test samples, respectively.
For SVHN, as the dataset [2]} is already imbalanced (i.e. class 1 and 2 have nearly 4\(\times \)  training images than other classes causing the vote counts biased towards these classes in PATE), we balance it with each class containing 5000 images Classes with training images \(< 5000\)  were left untouched..
We imbalance class 8 by performing a \(1:2\)  imbalance i.e. for every 2 images in each class, only 1 image in class 8. Therefore, class 8 is alloted 2500 training examples while other classes comprise of 5000 images each.
